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    <div class='section' id='section-0'>
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                <a href='#section-0'>#</a>
            </div>
            <h1>Finetune <a href="gpt2.html">GPT-2</a> with <a href="index.html">LoRA</a></h1>
<p>Here&#x27;s a Colab notebook for training a feedback transformer on Tiny Shakespeare dataset.</p>
<p><a href="https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/lora/experiment.ipynb"><img alt="Open In Colab" src="https://colab.research.google.com/assets/colab-badge.svg"></a></p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">14</span><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="lineno">15</span><span class="kn">from</span> <span class="nn">torch.optim</span> <span class="kn">import</span> <span class="n">Adam</span>
<span class="lineno">16</span><span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span><span class="p">,</span> <span class="n">TensorDataset</span>
<span class="lineno">17</span><span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">AutoTokenizer</span><span class="p">,</span> <span class="n">AutoModelForCausalLM</span>
<span class="lineno">18</span>
<span class="lineno">19</span><span class="kn">from</span> <span class="nn">labml</span> <span class="kn">import</span> <span class="n">lab</span><span class="p">,</span> <span class="n">monit</span><span class="p">,</span> <span class="n">tracker</span>
<span class="lineno">20</span><span class="kn">from</span> <span class="nn">labml.configs</span> <span class="kn">import</span> <span class="n">BaseConfigs</span><span class="p">,</span> <span class="n">option</span>
<span class="lineno">21</span><span class="kn">from</span> <span class="nn">labml.utils.download</span> <span class="kn">import</span> <span class="n">download_file</span>
<span class="lineno">22</span><span class="kn">from</span> <span class="nn">labml_nn.helpers.device</span> <span class="kn">import</span> <span class="n">DeviceConfigs</span>
<span class="lineno">23</span><span class="kn">from</span> <span class="nn">labml_nn.lora.gpt2</span> <span class="kn">import</span> <span class="n">GPTModel</span></pre></div>
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    <div class='section' id='section-1'>
        <div class='docs doc-strings'>
            <div class='section-link'>
                <a href='#section-1'>#</a>
            </div>
            <h2>Trainer configurations and the training loop</h2>
<p>The default configs can and will be over-ridden when we start the experiment</p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">26</span><span class="k">class</span> <span class="nc">Trainer</span><span class="p">(</span><span class="n">BaseConfigs</span><span class="p">):</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-2'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-2'>#</a>
            </div>
            
        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">32</span>    <span class="n">device</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">DeviceConfigs</span><span class="p">()</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-3'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-3'>#</a>
            </div>
            <p>GPT-2 configs </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">35</span>    <span class="n">layer_norm_epsilon</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1e-05</span>
<span class="lineno">36</span>    <span class="n">d_model</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">768</span>
<span class="lineno">37</span>    <span class="n">n_layers</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">12</span>
<span class="lineno">38</span>    <span class="n">n_heads</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">12</span>
<span class="lineno">39</span>    <span class="n">n_positions</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1024</span>
<span class="lineno">40</span>    <span class="n">vocab_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">50257</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-4'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-4'>#</a>
            </div>
            <p>Training configs </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">43</span>    <span class="n">epochs</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span>
<span class="lineno">44</span>    <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span>
<span class="lineno">45</span>    <span class="n">learning_rate</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1e-4</span>
<span class="lineno">46</span>    <span class="n">context_len</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">512</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-5'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-5'>#</a>
            </div>
            <p>LoRA rank </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">49</span>    <span class="n">lora_r</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-6'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-6'>#</a>
            </div>
            <p>Dataset </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">52</span>    <span class="n">text</span><span class="p">:</span> <span class="n">TensorDataset</span> <span class="o">=</span> <span class="s2">&quot;tiny_shakespeare&quot;</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-7'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-7'>#</a>
            </div>
            <p>Huggingface tokenizer </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">54</span>    <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">AutoTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;gpt2&quot;</span><span class="p">)</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-8'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-8'>#</a>
            </div>
            <p><a href="gpt2.html">GPT2 model</a> </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">56</span>    <span class="n">model</span><span class="p">:</span> <span class="n">GPTModel</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-9'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-9'>#</a>
            </div>
            <p>Optimizer </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">58</span>    <span class="n">optimizer</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-10'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-10'>#</a>
            </div>
            <p>Cross entropy loss </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">60</span>    <span class="n">loss_func</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-11'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-11'>#</a>
            </div>
            <p>Dataloader </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">62</span>    <span class="n">data_loader</span><span class="p">:</span> <span class="n">DataLoader</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-12'>
        <div class='docs doc-strings'>
            <div class='section-link'>
                <a href='#section-12'>#</a>
            </div>
            <h3>Load pre-trained <a href="https://huggingface.co/openai-community/gpt2">GPT-2 from huggingface</a></h3>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">64</span>    <span class="k">def</span> <span class="nf">_load_pretrained_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-13'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-13'>#</a>
            </div>
            <p>Load the huggingface model and get the parameters </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">70</span>        <span class="n">hf_model</span> <span class="o">=</span> <span class="n">AutoModelForCausalLM</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;gpt2&quot;</span><span class="p">)</span>
<span class="lineno">71</span>        <span class="n">state_dict</span> <span class="o">=</span> <span class="n">hf_model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-14'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-14'>#</a>
            </div>
            <p>Transformer embedding and prediction layer parameter mapping (<code  class="highlight"><span></span><span class="n">hf</span><span class="p">:</span> <span class="n">ours</span></code>
) </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">74</span>        <span class="n">mapping</span> <span class="o">=</span> <span class="p">{</span>
<span class="lineno">75</span>            <span class="s1">&#39;transformer.wte.weight&#39;</span><span class="p">:</span> <span class="s1">&#39;token_embedding.weight&#39;</span><span class="p">,</span>
<span class="lineno">76</span>            <span class="s1">&#39;transformer.wpe.weight&#39;</span><span class="p">:</span> <span class="s1">&#39;position_embedding.weight&#39;</span><span class="p">,</span>
<span class="lineno">77</span>            <span class="s1">&#39;transformer.ln_f.weight&#39;</span><span class="p">:</span> <span class="s1">&#39;final_norm.weight&#39;</span><span class="p">,</span>
<span class="lineno">78</span>            <span class="s1">&#39;transformer.ln_f.bias&#39;</span><span class="p">:</span> <span class="s1">&#39;final_norm.bias&#39;</span><span class="p">,</span>
<span class="lineno">79</span>            <span class="s1">&#39;lm_head.weight&#39;</span><span class="p">:</span> <span class="s1">&#39;lm_head.weight&#39;</span>
<span class="lineno">80</span>        <span class="p">}</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-15'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-15'>#</a>
            </div>
            <p>Mapping (<code  class="highlight"><span></span><span class="n">hf</span><span class="p">:</span> <span class="n">ours</span></code>
) of decoder layers </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">83</span>        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">):</span>
<span class="lineno">84</span>            <span class="n">mapping</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;transformer.h.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.ln_1.weight&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;blocks.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.attn_norm.weight&#39;</span>
<span class="lineno">85</span>            <span class="n">mapping</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;transformer.h.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.ln_1.bias&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;blocks.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.attn_norm.bias&#39;</span>
<span class="lineno">86</span>            <span class="n">mapping</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;transformer.h.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.attn.c_attn.weight&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;blocks.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.attn.qkv_projection.weight&#39;</span>
<span class="lineno">87</span>            <span class="n">mapping</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;transformer.h.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.attn.c_attn.bias&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;blocks.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.attn.qkv_projection.bias&#39;</span>
<span class="lineno">88</span>            <span class="n">mapping</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;transformer.h.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.attn.c_proj.weight&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;blocks.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.attn.output_projection.weight&#39;</span>
<span class="lineno">89</span>            <span class="n">mapping</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;transformer.h.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.attn.c_proj.bias&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;blocks.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.attn.output_projection.bias&#39;</span>
<span class="lineno">90</span>            <span class="n">mapping</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;transformer.h.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.ln_2.weight&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;blocks.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.ffn_norm.weight&#39;</span>
<span class="lineno">91</span>            <span class="n">mapping</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;transformer.h.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.ln_2.bias&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;blocks.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.ffn_norm.bias&#39;</span>
<span class="lineno">92</span>            <span class="n">mapping</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;transformer.h.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.mlp.c_fc.weight&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;blocks.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.ffn.linear_in.weight&#39;</span>
<span class="lineno">93</span>            <span class="n">mapping</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;transformer.h.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.mlp.c_fc.bias&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;blocks.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.ffn.linear_in.bias&#39;</span>
<span class="lineno">94</span>            <span class="n">mapping</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;transformer.h.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.mlp.c_proj.weight&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;blocks.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.ffn.linear_out.weight&#39;</span>
<span class="lineno">95</span>            <span class="n">mapping</span><span class="p">[</span><span class="sa">f</span><span class="s1">&#39;transformer.h.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.mlp.c_proj.bias&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;blocks.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.ffn.linear_out.bias&#39;</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-16'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-16'>#</a>
            </div>
            <p>Move the parameters based on mapping </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">98</span>        <span class="n">new_state_dict</span> <span class="o">=</span> <span class="p">{}</span>
<span class="lineno">99</span>        <span class="k">for</span> <span class="n">old_key</span><span class="p">,</span> <span class="n">new_key</span> <span class="ow">in</span> <span class="n">mapping</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="lineno">100</span>            <span class="k">if</span> <span class="n">old_key</span> <span class="ow">in</span> <span class="n">state_dict</span><span class="p">:</span>
<span class="lineno">101</span>                <span class="n">new_state_dict</span><span class="p">[</span><span class="n">new_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">state_dict</span><span class="p">[</span><span class="n">old_key</span><span class="p">]</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-17'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-17'>#</a>
            </div>
            <p>GPT-2 hugging face uses 1D Convolution layers. We need to transpose those weights since we use linear layers </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">104</span>        <span class="n">convo_layers</span> <span class="o">=</span> <span class="p">([</span><span class="sa">f</span><span class="s1">&#39;blocks.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.ffn.linear_in.weight&#39;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)]</span> <span class="o">+</span>
<span class="lineno">105</span>                        <span class="p">[</span><span class="sa">f</span><span class="s1">&#39;blocks.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.ffn.linear_out.weight&#39;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)]</span> <span class="o">+</span>
<span class="lineno">106</span>                        <span class="p">[</span><span class="sa">f</span><span class="s1">&#39;blocks.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.attn.qkv_projection.weight&#39;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)]</span> <span class="o">+</span>
<span class="lineno">107</span>                        <span class="p">[</span><span class="sa">f</span><span class="s1">&#39;blocks.</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">.attn.output_projection.weight&#39;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">)])</span>
<span class="lineno">108</span>
<span class="lineno">109</span>        <span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="n">convo_layers</span><span class="p">:</span>
<span class="lineno">110</span>            <span class="n">new_state_dict</span><span class="p">[</span><span class="n">layer</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">new_state_dict</span><span class="p">[</span><span class="n">layer</span><span class="p">],</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-18'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-18'>#</a>
            </div>
            <p>Load out model. We use <code  class="highlight"><span></span><span class="n">strict</span> <span class="o">=</span> <span class="kc">False</span></code>
 because the state does not have LoRA weights </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">113</span>        <span class="n">missing_keys</span><span class="p">,</span> <span class="n">unexpected_keys</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">new_state_dict</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-19'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-19'>#</a>
            </div>
            <p>make sure that only lora weights are not loaded </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">116</span>        <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="s1">&#39;lora&#39;</span> <span class="ow">in</span> <span class="n">key</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">missing_keys</span><span class="p">)</span>
<span class="lineno">117</span>        <span class="k">assert</span> <span class="ow">not</span> <span class="n">unexpected_keys</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-20'>
        <div class='docs doc-strings'>
            <div class='section-link'>
                <a href='#section-20'>#</a>
            </div>
            <h3>Initialize the model, optimizer and dataloader</h3>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">119</span>    <span class="k">def</span> <span class="nf">initialize</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-21'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-21'>#</a>
            </div>
            <p>Initialize the <a href="gpt2.html">GPT2 model</a> </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">124</span>        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">GPTModel</span><span class="p">(</span>
<span class="lineno">125</span>            <span class="n">layer_norm_epsilon</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">layer_norm_epsilon</span><span class="p">,</span>
<span class="lineno">126</span>            <span class="n">d_model</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">d_model</span><span class="p">,</span>
<span class="lineno">127</span>            <span class="n">n_layers</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_layers</span><span class="p">,</span>
<span class="lineno">128</span>            <span class="n">n_heads</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_heads</span><span class="p">,</span>
<span class="lineno">129</span>            <span class="n">n_positions</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_positions</span><span class="p">,</span>
<span class="lineno">130</span>            <span class="n">vocab_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">vocab_size</span><span class="p">,</span>
<span class="lineno">131</span>            <span class="n">r</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lora_r</span><span class="p">,</span>
<span class="lineno">132</span>        <span class="p">)</span>
<span class="lineno">133</span>        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-22'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-22'>#</a>
            </div>
            <p>Load pre-trained model weights </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">135</span>        <span class="bp">self</span><span class="o">.</span><span class="n">_load_pretrained_weights</span><span class="p">()</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-23'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-23'>#</a>
            </div>
            <p>Initialize the optimizer </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">138</span>        <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">Adam</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">learning_rate</span><span class="p">)</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-24'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-24'>#</a>
            </div>
            <p>Initialize the data loader </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">141</span>        <span class="bp">self</span><span class="o">.</span><span class="n">data_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">text</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-25'>
        <div class='docs doc-strings'>
            <div class='section-link'>
                <a href='#section-25'>#</a>
            </div>
            <h3>Training loop</h3>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">143</span>    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-26'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-26'>#</a>
            </div>
            
        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">148</span>        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">monit</span><span class="o">.</span><span class="n">loop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">epochs</span><span class="p">):</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-27'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-27'>#</a>
            </div>
            <p><code  class="highlight"><span></span><span class="n">inputs</span></code>
 has shape <code  class="highlight"><span></span><span class="p">[</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">seq_len</span><span class="p">]</span></code>
 </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">150</span>            <span class="k">for</span> <span class="p">(</span><span class="n">inputs</span><span class="p">,)</span> <span class="ow">in</span> <span class="n">monit</span><span class="o">.</span><span class="n">iterate</span><span class="p">(</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_loader</span><span class="p">):</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-28'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-28'>#</a>
            </div>
            <p>Move <code  class="highlight"><span></span><span class="n">inputs</span></code>
 to device </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">152</span>                <span class="n">inputs</span> <span class="o">=</span> <span class="n">inputs</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-29'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-29'>#</a>
            </div>
            <p>Call the model, with the all but the last token </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">154</span>                <span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">inputs</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-30'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-30'>#</a>
            </div>
            <p>Get cross entropy loss </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">156</span>                <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss_func</span><span class="p">(</span><span class="n">logits</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">logits</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">inputs</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-31'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-31'>#</a>
            </div>
            <p>Make gradients 0 </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">159</span>                <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-32'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-32'>#</a>
            </div>
            <p>Compute gradients </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">161</span>                <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-33'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-33'>#</a>
            </div>
            <p>Optimize </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">163</span>                <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-34'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-34'>#</a>
            </div>
            <p>Log the loss </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">166</span>                <span class="n">tracker</span><span class="o">.</span><span class="n">save</span><span class="p">({</span><span class="s1">&#39;loss&#39;</span><span class="p">:</span> <span class="n">loss</span><span class="p">})</span>
<span class="lineno">167</span>                <span class="n">tracker</span><span class="o">.</span><span class="n">add_global_step</span><span class="p">()</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-35'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-35'>#</a>
            </div>
            <p> </p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">169</span>            <span class="n">tracker</span><span class="o">.</span><span class="n">new_line</span><span class="p">()</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-36'>
        <div class='docs doc-strings'>
            <div class='section-link'>
                <a href='#section-36'>#</a>
            </div>
            <h3>Tiny Shakespeare dataset</h3>
<p>It will download from the url if not present</p>

        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">172</span><span class="nd">@option</span><span class="p">(</span><span class="n">Trainer</span><span class="o">.</span><span class="n">text</span><span class="p">)</span>
<span class="lineno">173</span><span class="k">def</span> <span class="nf">tiny_shakespeare</span><span class="p">(</span><span class="n">c</span><span class="p">:</span> <span class="n">Trainer</span><span class="p">):</span></pre></div>
        </div>
    </div>
    <div class='section' id='section-37'>
        <div class='docs'>
            <div class='section-link'>
                <a href='#section-37'>#</a>
            </div>
            
        </div>
        <div class='code'>
            <div class="highlight"><pre><span class="lineno">179</span>    <span class="n">path</span> <span class="o">=</span> <span class="n">lab</span><span class="o">.</span><span class="n">get_data_path</span><span class="p">()</span> <span class="o">/</span> <span class="s1">&#39;tiny_shakespeare.txt&#39;</span>
<span class="lineno">180</span>    <span class="k">if</span> <span class="ow">not</span> <span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">():</span>
<span class="lineno">181</span>        <span class="n">download_file</span><span class="p">(</span><span class="s2">&quot;https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt&quot;</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span>
<span class="lineno">182</span>    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf-8&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="lineno">183</span>        <span class="n">text</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="lineno">184</span>
<span class="lineno">185</span>    <span class="n">tokens</span> <span class="o">=</span> <span class="n">c</span><span class="o">.</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">encode</span><span class="p">(</span><span class="n">text</span><span class="p">)</span>
<span class="lineno">186</span>    <span class="n">num_batches</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">tokens</span><span class="p">)</span> <span class="o">//</span> <span class="p">(</span><span class="n">c</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">*</span> <span class="n">c</span><span class="o">.</span><span class="n">context_len</span><span class="p">)</span>
<span class="lineno">187</span>    <span class="n">tokens</span> <span class="o">=</span> <span class="n">tokens</span><span class="p">[:</span><span class="n">num_batches</span> <span class="o">*</span> <span class="n">c</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">*</span> <span class="n">c</span><span class="o">.</span><span class="n">context_len</span><span class="p">]</span>
<span class="lineno">188</span>    <span class="n">input_ids</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">tokens</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">c</span><span class="o">.</span><span class="n">context_len</span><span class="p">)</span>
<span class="lineno">189</span>    <span class="k">return</span> <span class="n">TensorDataset</span><span class="p">(</span><span class="n">input_ids</span><span class="p">)</span></pre></div>
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    }

    function handleImage(img) {
        img.parentElement.style.textAlign = 'center'

        var modal = document.createElement('div')
        modal.id = 'modal'

        var modalContent = document.createElement('div')
        modal.appendChild(modalContent)

        var modalImage = document.createElement('img')
        modalContent.appendChild(modalImage)

        var span = document.createElement('span')
        span.classList.add('close')
        span.textContent = 'x'
        modal.appendChild(span)

        img.onclick = function () {
            console.log('clicked')
            document.body.appendChild(modal)
            modalImage.src = img.src
        }

        span.onclick = function () {
            document.body.removeChild(modal)
        }
    }

    handleImages()
</script>
</body>
</html>